GENERALIZED INVERSE GROUP OF SIGNAL AND ITS IMPLEMENTATION WITH NEURAL NETWORKS

A new concept, the generalized inverse group (GIG) of signal, is firstly proposed and its properties, leaking coefficients and implementation with neural networks are presented. Theoretical analysis and computational simulation have shown that (1) there is a group of finite length of generalized inverse signals for any given finite signal, which forms the GIG; (2) each inverse group has different leaking coefficients, thus different abnormal states; (3) each GIG can be implemented by a grouped and improved single-layer perceptron which appears with fast convergence. When used in deconvolution, the proposed GIG can form a new parallel finite length of filtering deconvolution method. On off-line processing, the computational time is reduced to O(N) from O(N2). And the less the leaking coefficient is, the more reliable the deconvolution will be.     

Estimation of Aeroelastic Parameters of Bridge Decks Using Neural Networks

A new method of estimating flutter derivatives using artificial neural networks is proposed. Unlike other computational fluid dynamics based numerical analyses, the proposed method estimates flutter derivatives utilizing previously measured experimental data. One of the advantages of the neural networks approach is that they can approximate a function of many dimensions. An efficient method has been developed to quantify the geometry of deck sections for neural network input. The output of the neural network is flutter derivatives. The flutter derivatives estimation network, which has been trained by the proposed methodology, is tested both for training sets and novel testing sets. The network shows reasonable performance for the novel sets, as well as outstanding performance for the training sets. Two variations of the proposed network are also presented, along with their estimation capability. The paper shows the potential of applying neural networks to wind force approximations.     

NEURAL NETS SPEED FLOW CALCULATION

Neural Nets Successfully Solve Complex Fluid Flow Problem. Much research is being done in the area of neural networks, and industry is actively seeking successful application to real world problems. We describe here a successful application. We have used neural networks to model complex coolant flow patterns, such as those encountered in design of hypersonic aircraft. Previous calculation methods, while reasonably accurate, are iterative and extremely time consuming. Our new approach uses a hierarchical neural network architecture to model coolant flow distribution in multiple heat exchanger panels. This method is direct, fast, and accurate.     

Seismic safety of lifelines—an optimum design method

Lifeline systems are vulnerable to two types of hazards arising from potential earthquake sources. These are the hazard of a fault-rupture strike on elements of a lifeline system and the hazard of overstress induced in different elements of the system because of the ground vibration. An optimum design method is presented in this paper for the design of a lifeline system for a maximum accepted probability of failure because of any of the two modes of failure. The method may be used to determine an optimum path between a number of fixed points which represent supply or demand stations in the system.     

Transformations in machining. Part 1. enhancement of wavelet transformation neural network (WT-NN) combination with a preprocessor

Properly selected transformation methods obtain the most significant characteristics of metal cutting data efficiently and simplify the classification. Wavelet Transformation (WT) and Neural Networks (NN) combination was used to classify the experimental cutting force data of milling operations previously. Preprocessing (PreP) of the approximation coefficients of the WT is proposed just before the classification by using the Adaptive Resonance Theory (ART2) type NNs. Genetic Algorithm (GA) was used to estimate the weights of each coefficient of the PreP. The WT-PreP-NN (ART2) combination worked at lower vigilances by creating only a few meaningful categories without any errors. The WT-NN (ART2) combination could obtain the same error rate only if very high vigilances are used and many categories are allowed.     

图像压缩编码方法分析

介绍图像压缩编码技术发展过程,对图像压缩编码,特别是自适应预测编码、模型法编码、分形编码、小波变换压缩编码、神经网络压缩编码原理和特点进行分析,并阐述图像压缩编码的作用.     

人类HLA-DRB1基因型与民族和人群的神经网络无监督模式分类

HLA-DRB1等位基因主要参与人类的抗原免疫功能,对其进行比较研究,有助于追溯人类的进化迁移史,开发类群特异性药物.本研究以Matlab为平台,用自组织竞争网络(Self-Organizing Competitive Neural Network),对世界54个民族和人群、14个HLA-DRB1等位基因,进行了无监督模式分类.结果表明,各民族之间存在差异性,同民族的各人群之间有相似性.西伯利亚各人群,澳洲各土著人群,黑人各人群,南美印第安各人群,犹太族各人群,日本各人群,及欧美白人各人群有相对独立性:南美印第安人与西伯利亚人有高度相似性;中国民族在南方人群和北方人群间存在较大差异;中国汉族中,广东汉族地位特殊,与少数民族如拉祜族、瑶族关系密切.     

Complex system reliability modelling with Dynamic Object Oriented Bayesian Networks (DOOBN)

Nowadays, the complex manufacturing processes have to be dynamically modelled and controlled to optimise the diagnosis and the maintenance policies. This article presents a methodology that will help developing Dynamic Object Oriented Bayesian Networks (DOOBNs) to formalise such complex dynamic models. The goal is to have a general reliability evaluation of a manufacturing process, from its implementation to its operating phase. The added value of this formalisation methodology consists in using the a priori knowledge of both the system's functioning and malfunctioning. Networks are built on principles of adaptability and integrate uncertainties on the relationships between causes and effects. Thus, the purpose is to evaluate, in terms of reliability, the impact of several decisions on the maintenance of the system. This methodology has been tested, in an industrial context, to model the reliability of a water (immersion) heater system.     

Brokers and bricoleurs: entrepreneurship in Wales' online music scene

 The power of some new entrants to the music industry derives from their position as brokers in computer-mediated environments. Brokers act instrumentally to exploit their position within a network which, in turn, depends on their ability to build and sustain links (and, in computer-mediated environments, hyperlinks). Bricolage in computer-mediated entrepreneurship refers to the intuitive manipulation of resources in order to achieve (perhaps tacit) goals. Without careful stewardship of the new intellectual wealth thus created, bricolage may profit neither the individual nor the wider community. Accepted 14 March 2002     

Convective cooling of supercritical carbon dioxide inside tubes: heat transfer analysis through neural networks

In a previous work, convective heating of carbon dioxide was studied with neural networks (NN), obtaining a totally heuristical heat transfer equation from the direct regression of experimental data. In the present work, the analysis focuses on the cooling process, which has a technical relevance in various applications, as for example in transcritical refrigeration cycles. Heat transfer around the critical zone presents a marked enhancement, that follows the peaks in thermophysical properties like thermal conductivity and heat capacity. Similarly, other properties like density and enthalpy, present a strong variation in narrow temperature intervals around the critical point.This constitutes then a highly non-linear phenomenon, for which it is advisable to use a very flexible function approximator like the NNs. NN models were applied both in terms of dimensionless numbers and of physical quantities, obtaining the two corresponding NN architectures. The choice of the optimal number of neurons in the NN hidden layer is discussed. The NN models are then compared with a recent correlation from literature, for which the validation results present an AAD of 27% and a bias of −26% with an evident prediction shifting. On the other hand the NN models in terms of dimensionless numbers and of physical quantities have AAD and bias of 14% and −4%, and of 7% and −2%, respectively, showing a largely better performance.